Research Of The Image Compression Based On Context

With the rapid development of digital communication and multimedia technology, image compression has played an important role in signal transmission and storage. At present, there are many kinds of image compression. In recent years, the compression method based on context has become a new study point. This method is greatly used in the field of image compression because of its high compression performance and clear restored image.In this dissertation, the further research about the image compression based on context is made. It includes the following three aspects.Firstly, in order to improve the drawbacks of the current downsample-based compression having high complexity and bad reconstruction for edge region, we propose an image compression with adaptive downsampling and lapped transform at the low bit rates. At the encoder side, codec can choose adaptively the smooth area of images to downsample according to the DCT coefficients; and at the decoder side, codec uses simple Cubic interpolation to restore the downsampled area. In addition, in view of that DCT can not remove the cross-block correlation, so lapped transform is adopted in this new method. Experimental results show that the method has not only the high compression performance but also the low complexity.Secondly, aimed at the problem that the conventional quad-tree coding uses fixed scanning order and equal-length bit to code a significant block, a new variable-length quad-tree coding method with context weight and rate-distortion optimization is proposed in this paper. The method can adjust the scanning order of sub-blocks using their significance degrees which are predicted by the proposed context weight model, and spend different-length bits to represent different cases. In addition, the context weight model can also be used in rate-distortion optimization in order to further improve the compression performance.Finally, we propose a new image coding method with inverse quantization method based on context. This method is to estimate the distribution of transformed coefficients by the coefficients which have been decoded at the decoder side, and then find the optimal reconstructed point of the coefficients that will be decoded using the estimated distribution of transformed coefficients. This method makes us avoid using mean quantizer. With the inverse quantization technology based on context, our coding system satisfies a dissymmetrical compression scheme which keeps the complexity of the encoder invariable and uses the complex quantization method at the decoder side that has powerful computational ability to increase the compression performance.